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Image Contrast Enhancement Techniques: A Comparative Study of Performance

by Ismail A. Humied, Fatma E.Z. Abou-Chadi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 137 - Number 13
Year of Publication: 2016
Authors: Ismail A. Humied, Fatma E.Z. Abou-Chadi
10.5120/ijca2016908781

Ismail A. Humied, Fatma E.Z. Abou-Chadi . Image Contrast Enhancement Techniques: A Comparative Study of Performance. International Journal of Computer Applications. 137, 13 ( March 2016), 43-48. DOI=10.5120/ijca2016908781

@article{ 10.5120/ijca2016908781,
author = { Ismail A. Humied, Fatma E.Z. Abou-Chadi },
title = { Image Contrast Enhancement Techniques: A Comparative Study of Performance },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 137 },
number = { 13 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 43-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume137/number13/24444-2016908781/ },
doi = { 10.5120/ijca2016908781 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:38:20.194849+05:30
%A Ismail A. Humied
%A Fatma E.Z. Abou-Chadi
%T Image Contrast Enhancement Techniques: A Comparative Study of Performance
%J International Journal of Computer Applications
%@ 0975-8887
%V 137
%N 13
%P 43-48
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper the performance of four techniques for contrast enhancement of digital images was investigated. The techniques are: histogram equalization (HE), thresholded histogram equalization (WTHE), the low-complexity histogram modification algorithm (LCHM) and a newly developed technique which is a combination of two techniques (HEFGLG): the histogram equalization (HE) and the Fast Gray Level Grouping (FGLG). The performance was compared using different images (gray scale as well as colored) in order to identify which algorithm has the best performance across a variety of images from different sensors and having varying characteristics. Based on the visual quality and the quantitative measures: Absolute Mean Brightness Error (AMBE), the discrete entropy (H), and the measure of enhancement (EME). The experimental results showed that the HEFGLG algorithm outperforms other algorithms. It has the advantage that it has low time complexity since it is a combination of two techniques HE and FGLG, each has low time complexity.

References
  1. Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing (3rd Edition), Prentice Hall, August 2008.
  2. Frank Y. Shih J, Image Processing and Pattern Recognition Fundamentals and Techniques, the Institute of Electrical and Electronics Engineers, Inc, Hoboken, New Jersey- Canada, Wiley and Sons, 2010.
  3. R. Hummel, ‘Histogram modification techniques’, Computer Graphics and Image Processing, vol.4, no.3, pp. 209–224, 1975
  4. Senthilkumaran, N., Thimmiaraja, J.: Histogram equalization for image enhancement using MRI brain images. In: 2014 World Congress on Computing and Communication Technologies (WCCCT), pp. 80–83. IEEE, 2014.
  5. Kaur, M., Kaur, J., Kaur, J.: Survey of contrast enhancement techniques based on histogram equalization. International Journal of Advanced Computer Science and Applications, 2011.
  6. Q. Wang and R. K. Ward, “Fast image/video contrast enhancement based on weighted thresholded histogram equalization,” IEEE Trans. Consum. Electron., vol. 53, no. 2, pp. 757–764, May 2007.
  7. T. Arici, S. Dikbas and Y. Altunbasak, “A Histogram Modification Framework and Its Application for Image Contrast Enhancement,” IEEE Trans. Image. Process., vol. 18, no. 9, pp. 1921–1935, Sept 2009.
  8. Ismail A. Humied, Fatma E.Z. Abou-Chadi and Magdy Z. Rashad, “A new combined technique for automatic contrast enhancement of digital images”, Egypt Inform J 2012.
  9. Z. Chen, B. R. Abidi, D. L. Page, and M. A. Abidi, “Gray-level grouping (GLG): an automatic method for optimized image contrast enhancement-part I: the basic method,” IEEE Transactions on Image Processing, vol. 15, no. 8, pp. 2290– 2302, Aug. 2006.
  10. G. Ravichandran and V. Magudeeswaran, “An Efficient Method for Contrast Enhancement in Still Images using Histogram Modification Framework,” Journal of Computer Science , no. 5, P. 775-779, 2012.
  11. Yen-Ching Chang and Chun-Ming Chang, , ‘A Simple Histogram Modification Scheme for Contrast Enhancement’, IEEE Transactions on Consumer Electronics, Vol. 56, No. 2., 2010.
  12. S.-D. Chen and A. Ramli, “Minimum mean brightness error bi-histogram equalization in contrast enhancement,” IEEE Trans. Consum. Electron., vol. 49, no. 4, pp. 1310–1319, Apr. 2003.
  13. Saravanan S, and P.Siva Kumar, “Image Contrast Enhancement Using Histogram Equalization Techniques: Review,” International Journal of Advances in Computer Science and Technology, vol. 3, no. 3, pp. 163–172, March 2014.
Index Terms

Computer Science
Information Sciences

Keywords

Histogram Equalization Low-complexity histogram modification Weighted-thresholded histogram equalization Combined algorithm

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